{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explaining Performance Drivers\n",
    "### Summary \n",
    "\n",
    "In this note I team up Maxim Fedotov from Rates Structuring Strats to take a closer look at hedging a popular bond trade and present a new framework for explaining pnl that can be applied across any trade or portfolio in `gs_quant`. Traders, PMs, risk managers or operationally oriented users can deploy this framework to improve their understanding of historical performance drivers and in turn drive better hedging, risk management and cash management decisions.\n",
    "\n",
    "While foreign fixed income assets can present attractive investment opportunities, domestic investors may need to use overlays if they want to receive domestic currency. One popular example of this has been buying JGBs and using a fixfix swap to receive in local. In this bond + swap package, FX of the swap will fully match the bond FX component but clients can still face pnl volatility due to basis and IR differences not matched by the bond accounted at cost.\n",
    "\n",
    "In this notebook I will take a closer look at the fixfix swap tailored for a specific JGB bond and decompose its historical pnl drivers into rates, cross currency, fx and cashflow components to better understand the drivers of this volatility.\n",
    "\n",
    "\n",
    "The content of this notebook is split into:\n",
    "* [1 - Let's get started with gs quant](#1---Let's-get-started-with-gs-quant)\n",
    "* [2 - Model bond as FixFix swap](#2---Model-bond-as-fixfix-swap)\n",
    "* [3 - Attribute pnl and calculate cashflows](#3---Attribute-pnl-and-calculate-cashflows)\n",
    "* [4 - Putting it all together](#4---Putting-it-all-together)\n",
    "* [What's New](#What's-New)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Let's get started with gs quant\n",
    "Start every session with authenticating with your unique client id and secret. If you don't have a registered app, create one [here](https://marquee.gs.com/s/developer/myapps/register). `run_analytics` scope is required for the risk functionality and `read_product_data` is required for pulling data covered in this example. Below produced using gs-quant version 0.8.155."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gs_quant.session import GsSession\n",
    "GsSession.use(client_id=None, client_secret=None, scopes=('run_analytics', 'read_product_data')) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Model bond as fixfix swap\n",
    "\n",
    "Let's pick a JGB bond to analyze - in this example we will look at `JGB #53 JP1300531GC0` (ISIN) and fill in the relative details. Note here we are manually inputting the details but we'll remove this step once you're able to model bonds in gs quant directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date\n",
    "\n",
    "bond_notional = 1e8\n",
    "bond_coupon = 0.006\n",
    "coupon_freq = '6m'\n",
    "bond_maturity = date(2046, 12, 20)\n",
    "last_cpn_date = date(2018, 12, 20) # Last paid coupon date as seen from backtest start date\n",
    "maturity = bond_maturity\n",
    "bond_dirty_price = 97.66\n",
    "\n",
    "# historical window we'll examine\n",
    "start_date = date(2019, 1, 2)\n",
    "end_date = date(2019, 11, 1)\n",
    "CSA = 'EUR-OIS'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, in order to receive payments in local - let's say that is EUR rather than JPY - we can structure a `IRXccySwapFixFix` swap that matches our bond's characteristics outlined above.\n",
    "\n",
    "To do this, we need to size it to the bond notional using the EUR/JPY FX rate at the start of our window. Let's pull it from [Marquee data catalogue](https://marquee.gs.com/s/discover/data-services/catalog) as a first step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gs_quant.data import Dataset\n",
    "\n",
    "ds = Dataset('FXSPOT_STANDARD')\n",
    "eurjpy_data = ds.get_data(start_date, end_date, bbid='JPYEUR')\n",
    "fx_rate = eurjpy_data.loc[start_date].spot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the FX spot as of `start_date` in hand, let's define our swap and `resolve()` to fix any relative parameters as of the same `start date`. We can use `as_dict()` to view what these are. I'll also calculate swap cashflows here that we will use later to add to the total pv we're attributing. Note below we can choose between par or proceeds asset swap format that impact the fixed notional and fee used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'termination_date': datetime.date(2046, 12, 20),\n",
       " 'payer_day_count_fraction': ACT/ACT ISDA,\n",
       " 'receiver_rate': 0.01795441569651901,\n",
       " 'fee': 0.0,\n",
       " 'fee_currency': JPY,\n",
       " 'payer_rate': 0.006,\n",
       " 'type': XccySwapFixFix,\n",
       " 'receiver_business_day_convention': Modified Following,\n",
       " 'payer_frequency': '6m',\n",
       " 'notional_amount': 100000000.0,\n",
       " 'receiver_frequency': '1y',\n",
       " 'principal_exchange': Last,\n",
       " 'asset_class': Rates,\n",
       " 'receiver_day_count_fraction': 30/360,\n",
       " 'receiver_currency': EUR,\n",
       " 'effective_date': datetime.date(2018, 12, 20),\n",
       " 'fee_payment_date': datetime.date(2019, 1, 7),\n",
       " 'receiver_notional_amount': 788340.8179999999,\n",
       " 'payer_currency': JPY,\n",
       " 'payer_business_day_convention': Modified Following}"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from gs_quant.markets import PricingContext\n",
    "from gs_quant.instrument import IRXccySwapFixFix\n",
    "from gs_quant import risk\n",
    "\n",
    "ASWType = 'Proceeds' # Can either be par or proceeds\n",
    "fixed_notional = bond_notional * fx_rate *  bond_dirty_price / 100 if ASWType=='Proceeds' else bond_notional * fx_rate\n",
    "fee = 0 if ASWType=='Proceeds' else -(bond_dirty_price - 100) / 100 * bond_notional\n",
    "\n",
    "swap = IRXccySwapFixFix(effective_date=last_cpn_date, termination_date=bond_maturity, notional_amount=bond_notional, \n",
    "                        payer_currency='JPY', receiver_notional_amount=fixed_notional, payer_rate=bond_coupon,\n",
    "                        receiver_currency='EUR', payer_frequency=coupon_freq, receiver_frequency='1y',\n",
    "                        payer_day_count_fraction='act/act ISDA', fee=fee, receiver_rate='ATM')\n",
    "    \n",
    "with PricingContext(pricing_date=start_date, market_data_location='LDN', csa_term=CSA):\n",
    "    swap.resolve()\n",
    "    cf = swap.calc(risk.Cashflows)\n",
    "swap.as_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Attribute pnl and calculate cashflows\n",
    "\n",
    "Now, let's break out our swap pv into contributions from various drivers - to do this, we'll use a newly minted gs-quant analytic called `PnlExplain` which attributes the change in value of an individual trade (or portfolio) to market moves. Note the values returned are in USD. In the below, I look at `PnLExplain` every day to get a daily attribution but you can use this for any time period. I also group by`mkt_type` for clarity but you can use the measure to get a more granular view by removing the grouping. You'll notice a `CROSSES` PnL, which represents the cross-effects among the other types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gs_quant.markets import CloseMarket\n",
    "\n",
    "result_dict = {}\n",
    "for d in eurjpy_data.index:\n",
    "    exp_measure = risk.PnlExplain(CloseMarket(date=d.date()))\n",
    "    with PricingContext(pricing_date=start_date, market_data_location='LDN', is_async=True, csa_term=CSA):\n",
    "        exp_res = swap.calc(exp_measure)\n",
    "    result_dict[d.date()] = exp_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>mkt_type</th>\n",
       "      <th>CROSSES</th>\n",
       "      <th>FX</th>\n",
       "      <th>FX FWD</th>\n",
       "      <th>IR</th>\n",
       "      <th>IR BASIS</th>\n",
       "      <th>IR CC</th>\n",
       "      <th>IR XC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>36.621530</td>\n",
       "      <td>-11980.639191</td>\n",
       "      <td>25.769065</td>\n",
       "      <td>-1120.076874</td>\n",
       "      <td>-20.614371</td>\n",
       "      <td>-0.103477</td>\n",
       "      <td>2672.806140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>-108.035684</td>\n",
       "      <td>-5073.445248</td>\n",
       "      <td>10.048568</td>\n",
       "      <td>-30625.813539</td>\n",
       "      <td>350.301729</td>\n",
       "      <td>-0.098469</td>\n",
       "      <td>3844.754112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>-115.654068</td>\n",
       "      <td>1844.805068</td>\n",
       "      <td>12.789388</td>\n",
       "      <td>-20658.964027</td>\n",
       "      <td>951.194210</td>\n",
       "      <td>-0.005372</td>\n",
       "      <td>3189.912983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>-89.085150</td>\n",
       "      <td>953.032055</td>\n",
       "      <td>8.299623</td>\n",
       "      <td>-19361.553933</td>\n",
       "      <td>-592.276210</td>\n",
       "      <td>0.002793</td>\n",
       "      <td>4873.192840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-09</th>\n",
       "      <td>-230.015429</td>\n",
       "      <td>4820.738883</td>\n",
       "      <td>17.457522</td>\n",
       "      <td>-16422.007642</td>\n",
       "      <td>-607.153929</td>\n",
       "      <td>-0.020474</td>\n",
       "      <td>1183.107531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "mkt_type       CROSSES            FX     FX FWD            IR    IR BASIS  \\\n",
       "date                                                                        \n",
       "2019-01-03   36.621530 -11980.639191  25.769065  -1120.076874  -20.614371   \n",
       "2019-01-04 -108.035684  -5073.445248  10.048568 -30625.813539  350.301729   \n",
       "2019-01-07 -115.654068   1844.805068  12.789388 -20658.964027  951.194210   \n",
       "2019-01-08  -89.085150    953.032055   8.299623 -19361.553933 -592.276210   \n",
       "2019-01-09 -230.015429   4820.738883  17.457522 -16422.007642 -607.153929   \n",
       "\n",
       "mkt_type       IR CC        IR XC  \n",
       "date                               \n",
       "2019-01-03 -0.103477  2672.806140  \n",
       "2019-01-04 -0.098469  3844.754112  \n",
       "2019-01-07 -0.005372  3189.912983  \n",
       "2019-01-08  0.002793  4873.192840  \n",
       "2019-01-09 -0.020474  1183.107531  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def format_res(r, d):\n",
    "    # here we group and sum by market type - you can get a more granular view when skipping this step\n",
    "    df = r.groupby('mkt_type').sum().reset_index()\n",
    "    df['date'] = d\n",
    "    return df.set_index('date')\n",
    "\n",
    "result_clean = pd.concat([format_res(r.result(), d) for d, r in result_dict.items() if len(r.result())])\n",
    "result = result_clean.groupby(['date', 'mkt_type']).sum().unstack()\n",
    "result.columns = result.columns.droplevel(0)\n",
    "result.loc[start_date] = 0  # first day is 0 pnl\n",
    "result.head()  # let's take a peak!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for the final bit - cashflows. We have already calculated our cashflows in [step 2](#2---Model-bond-as-fixfix-swap). Let's remove anything that's later than our `end_date` and convert the cashflow amount into USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "\n",
    "cfs = cf.result()\n",
    "cfs = cfs[cfs['payment_date'] < end_date]\n",
    "cash = pd.Series(cfs.payment_amount.values, index=cfs.payment_date)\n",
    "cash = (cash * eurjpy_data.spot).fillna(0)\n",
    "result['CASH'] = cash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Putting it all together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, with all the results in hand, let's take a look!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x121622b0>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result['Total'] = result.sum(axis=1)\n",
    "result.plot(figsize=(12, 8), title='PnL Drivers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see in the chart above, over the backtest period rates drive most of the positive swap performance while cross currency effects are largely a negative drag over the period. FX, although a small positive contributor through mid 2019, ultimately drives much of the negative contribution through the remainder of the year. Remember we are looking at the swap hedge only here so FX will be entirely offset by the bond in the bond+swap package.\n",
    "\n",
    "In this note we decomposed the fixfix swap but you can use this framework to analyse any trade or portfolio - looking forward to hearing your feedback!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What's New\n",
    "* `PnlExplain` which we covered in this note!\n",
    "* `Portfolio.from_frame` and `Portfolio.from_csv` to help map and represent your portfolio object from a dataframe or csv file.\n",
    "* `to_frame` to view complex results - see example [here](https://nbviewer.jupyter.org/github/goldmansachs/gs-quant/blob/master/gs_quant/examples/01_pricing_and_risk/00_rates/010014_spread_option_grid_pricing.ipynb)\n",
    "* Solvers for different fields to allow to solve for a strike or fixed rate such that PV=x. Examples to come - please reach out in the meantime."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
